Bibliographic record
Abstract
Edmond et al present an analysis of the potential impact of mobile health team (MHT) on the coverage of maternal and child health interventions in rural Afghanistan.1 These MHTs have been part of the repertoire of services of the Afghan health services since 2003 focused on remote and difficult to reach rural villages and outposts. Each MHT consisted of an officially accredited midwife, vaccinator and nurse and mainly provide basic maternal and child health services including immunisations to pregnant and postpartum women, and children aged under 5 years of age. In comparison with districts that did not receive MHT services, Edmond et al found significant increases in available district-based health information for antenatal care, measles vaccination and receipt of at least one recommended IMCI service for diarrhoea and pneumonia management. These are important findings given the context of populations and families affected by conflict and insecurity. There are limited data on the relative contribution of conflict and insecurity to maternal and child health and survival globally and what exists, strongly suggests that coverage of key interventions and survival are relatively poor in such settings.2 Afghanistan is a case in point with long-standing conflict and previous evaluations have shown major differences in coverage of interventions geographically and in relation to conflict intensity.3 These …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".